Poster — Wed Eve—28: Monte Carlo Study of the Influence of a Novel Transmission Detector on 6 MV Photon Beam
Bibliographic record
Abstract
The Monte Carlo technique is used to investigate the influence of a novel transmission detector (IBA Dosimetry, Germany) on a 6MV photon beam. The transmission detector may be used as an in vivo IMRT quality assurance tool, and therefore will be a source of contaminant electrons. This could potentially affect prescribed dose to patients. The linear accelerator together with the transmission detector and a plastic block tray for comparison, were modeled using BEAMnrc/EGSnrc. Electron fluence at different SSDs (70, 80, 90, 100 cm), electron energy spectra, electron angular distributions, and surface doses were calculated. Calculated data were validated against measurements using a fixed parallel plate chamber. Calculated surface dose for both open field and non‐open fields (i.e. TRD and block tray) agree within 3% of measurement. The fluence of contaminant electrons produced in the TRD and block tray fields increases at shorter SSD, but most electrons at shorter SSD are low energy electrons with large angular spread. These electrons contribute less to surface dose at larger SSD because they are either out‐scattered or absorbed in air. Surface doses at different SSDs for the block tray are higher than those of the TRD. Contribution of contaminant electrons to dose in the buildup region increases with increasing field size. For a field, the contribution of electrons is 9.8 % and 8.6% for block tray and TRD respectively, while for a smaller field is 0.35% and 0.75% for TRD and block tray respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".